Cross entropy loss

As explained in Natural Language Processing in Action, Second Edition

Log loss is a probabilistic prediction metric based on the negative logarithm of the probability assigned to the observed target.

Binary cross-entropy measures how well predicted probabilities match binary targets. Its logarithmic penalty makes confidently wrong predictions especially costly. section 5.2.5

Name classification

For a model predicting whether a name is labeled female, binary cross-entropy can weight each example by the name’s frequency in the dataset. section 5.2.5

Equation 5.2 Binary cross-entropy

Inspect the binary cross-entropy equation for a single classification probability. section 5.2.5

>>> loss_func_train = torch.nn.BCELoss(
...     weight=torch.Tensor(df[['count']][istrain].values))
>>> loss_func_test = torch.nn.BCELoss(                          #1
...     weight=torch.Tensor(df[['count']][~istrain].values))
>>> loss_func_train
BCELoss()

See PyTorch’s BCELoss configured with dataset-frequency weights. section 5.2.5

How Natural Language Processing in Action, Second Edition covers it

How deep it goes

Explains the logarithmic penalty for confident errors and shows frequency weighting and a PyTorch implementation.

Examples

Uses name-based sex classification and weights examples by their dataset frequencies.

What you can do after

Apply PyTorch BCELoss with frequency weights to train a sigmoid-output binary classifier.

How it connects

Connects sigmoid activation and binary classification to optimization with stochastic gradient descent.

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Related concepts

• classifieraddresses · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• negative log-likelihoodequivalent to · Negative log-likelihood is the negative logarithm of the likelihood a probabilistic model assigns to observed data, commonly used as an objective or loss to minimize.
• focal lossincludes · Focal loss is a classification loss that downweights well-classified examples and emphasizes difficult or misclassified examples, especially under class imbalance.
• uncertaintymeasures · Uncertainty is incomplete knowledge or confidence about a value, state, interpretation, behavior, or outcome.
• likelihoodequivalent to · A likelihood is the probability of observed data under a specified probabilistic model, regarded as a function of candidate hypotheses or parameter values.

This explainer was generated by Manning's knowledge graph from the text of Natural Language Processing in Action, Second Edition. The section links point to the parts of the book it draws on.